



'''simple docstring'''






from __future__ import annotations

from typing import Any

class 				a__:







    def __init__( self					:    Tuple		,     __snake_case					:    int      ):
      a		:   List[str]	  =	num_of_nodes
      a		:   list[list[int]]	  =	[]
      a		:   dict[int, int]	  =	{}







    def      lowercase_			( self					:    Dict		,     __snake_case					:    int		,     __snake_case					:    int		,     __snake_case					:    int      ):
      self.m_edges.append([u_node, v_node, weight]      )







    def      lowercase_			( self					:    List[Any]		,     __snake_case					:    int      ):

      if self.m_component[u_node] == u_node:
        return u_node
      return self.find_component(self.m_component[u_node]      )







    def      lowercase_			( self					:    Any		,     __snake_case					:    int      ):

      if self.m_component[u_node] != u_node:
        for k in self.m_component:
          a		:   Optional[Any]	  =	self.find_component(__snake_case      )







    def      lowercase_			( self					:    List[Any]		,     __snake_case					:    list[int]		,     __snake_case					:    int		,     __snake_case					:    int      ):

      if component_size[u_node] <= component_size[v_node]:
        a		:   int	  =	v_node
        component_size[v_node] += component_size[u_node]
        self.set_component(__snake_case      )

      elif component_size[u_node] >= component_size[v_node]:
        a		:   Optional[Any]	  =	self.find_component(__snake_case      )
        component_size[u_node] += component_size[v_node]
        self.set_component(__snake_case      )







    def      lowercase_			( self					:    Optional[Any]      ):
      a		:   Any	  =	[]
      a		:   Union[str, Any]	  =	0

      a		:   list[Any]	  =	[-1] * self.m_num_of_nodes

      # A list of components (initialized to all of the nodes)
      for node in range(self.m_num_of_nodes      ):
        self.m_component.update({node: node}      )
        component_size.append(1      )

      a		:   Dict	  =	self.m_num_of_nodes

      while num_of_components > 1:
        for edge in self.m_edges:
          a  ,      a  ,      a		:   Tuple	  =	edge

          a		:   Optional[Any]	  =	self.m_component[u]
          a		:   Dict	  =	self.m_component[v]

          if u_component != v_component:

            for component in (u_component, v_component):
              if (
                  minimum_weight_edge[component] == -1
                  or minimum_weight_edge[component][2] > w
              ):
                a		:   Optional[int]	  =	[u, v, w]

        for edge in minimum_weight_edge:
          if isinstance(__snake_case		,     __snake_case      ):
            a  ,      a  ,      a		:   List[Any]	  =	edge

            a		:   Union[str, Any]	  =	self.m_component[u]
            a		:   Any	  =	self.m_component[v]

            if u_component != v_component:
              mst_weight += w
              self.union(__snake_case		,     __snake_case		,     __snake_case      )
              print(F"""Added edge [{u} - {v}]\nAdded weight: {w}\n"""      )
              num_of_components -= 1

        a		:   Union[str, Any]	  =	[-1] * self.m_num_of_nodes
      print(F"""The total weight of the minimal spanning tree is: {mst_weight}"""      )
def  lowerCamelCase__			(		):
  pass


if __name__ == "__main__":
     import doctest

     doctest.testmod()




'''simple docstring'''






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool


if is_vision_available():
     from PIL import Image

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       """naver-clova-ix/donut-base-finetuned-docvqa"""
    lowercase__					=       (
        """This is a tool that answers a question about an document (pdf). It takes an input named `document` which """
        """should be the document containing the information, as well as a `question` that is the question about the """
        """document. It returns a text that contains the answer to the question."""
    )
    lowercase__					=       """document_qa"""
    lowercase__					=       AutoProcessor
    lowercase__					=       VisionEncoderDecoderModel

    lowercase__					=       ["""image""", """text"""]
    lowercase__					=       ["""text"""]







    def __init__( self					:    str		,     *__snake_case					:    Any		,     **__snake_case					:    List[Any]      ):
      if not is_vision_available():
        raise ValueError('Pillow must be installed to use the DocumentQuestionAnsweringTool.'      )

      super().__init__(*__snake_case		,     **__snake_case      )







    def      lowercase_			( self					:    Union[str, Any]		,     __snake_case					:    "Image"		,     __snake_case					:    str      ):
      a		:   Union[str, Any]	  =	'<s_docvqa><s_question>{user_input}</s_question><s_answer>'
      a		:   Any	  =	task_prompt.replace('{user_input}'		,     __snake_case      )
      a		:   Dict	  =	self.pre_processor.tokenizer(
          __snake_case		,     add_special_tokens=__snake_case		,     return_tensors='pt'      ).input_ids
      a		:   Tuple	  =	self.pre_processor(__snake_case		,     return_tensors='pt'      ).pixel_values

      return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}







    def      lowercase_			( self					:    Any		,     __snake_case					:    int      ):
      return self.model.generate(
          inputs['pixel_values'].to(self.device      )		,     decoder_input_ids=inputs['decoder_input_ids'].to(self.device      )		,     max_length=self.model.decoder.config.max_position_embeddings		,     early_stopping=__snake_case		,     pad_token_id=self.pre_processor.tokenizer.pad_token_id		,     eos_token_id=self.pre_processor.tokenizer.eos_token_id		,     use_cache=__snake_case		,     num_beams=1		,     bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]		,     return_dict_in_generate=__snake_case		,     ).sequences







    def      lowercase_			( self					:    List[str]		,     __snake_case					:    Tuple      ):
      a		:   str	  =	self.pre_processor.batch_decode(__snake_case      )[0]
      a		:   str	  =	sequence.replace(self.pre_processor.tokenizer.eos_token		,     ''      )
      a		:   Union[str, Any]	  =	sequence.replace(self.pre_processor.tokenizer.pad_token		,     ''      )
      a		:   List[str]	  =	re.sub(r'<.*?>'		,     ''		,     __snake_case		,     count=1      ).strip()  # remove first task start token
      a		:   Dict	  =	self.pre_processor.tokenajson(__snake_case      )

      return sequence["answer"]